NSF Postdoctoral Fellowship in Biology FY 2020: Predicting Evolution Through Quantitative Dissection of the Bacterial Proteome

该行动资助了NSF 2020财年生物学博士后研究奖学金，研究基因组，环境和表型之间相互作用的生命规则的综合研究。该奖学金支持将以创新方式对生活规则领域作出贡献的研究员的研究和培训。这项研究将使用现代技术来预测细菌将如何进化以应对环境压力，这是了解抗生素耐药性等危险特征进化所必需的能力。虽然生物体的基因序列记录了其生命史，但仅凭序列很难预测生物体将如何进化。这是由于无法预测突变如何影响生物体的繁殖能力，即适应性。该项目将利用最近发现的生物学规律来探索突变如何改变细胞的能量预算以制造新的蛋白质。这些信息将用于建立一个数学模型，以了解突变是如何与适应性联系在一起的。这项工作将开发一个实验系统，可以控制和预测细菌的进化，这可能在医学和农业中有实际应用。这个项目将通过新技术的应用来增加研究员的多元化背景。为了扩大这项工作的影响，该研究员将在赞助科学家的实验室培训本科生和研究生。最近发展的细菌“生长规律”预测了细胞生长速度和核糖体专用的蛋白质组学部分之间的强烈相关性。这意味着适应性进化应该使细胞资源的分配偏向于维持核糖体而不是不必要的蛋白质。这项工作的目的是剖析适应性突变如何调节资源的分配，以最大限度地提高生长速度，最终建立一个数学模型来预测进化。该研究员将依靠新的基于测序的实验，允许对大量微生物种群中出现的新的有益突变进行时间分辨测量。这些有益突变对蛋白质组组成的影响将通过质谱和rna测序进行监测，阐明有益突变如何改变资源分配。给定一个有益突变的身份和基因表达谱的成对映射，该研究员旨在定量绘制适应度景观。这样就有可能预测基因表达的变化如何导致适应性的变化。该研究员在建模和实验方面的背景将通过基于测序的实验的开发和应用得到加强。更广泛的影响将包括训练本科生和研究生设计、执行和解释项目实验。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This action funds an NSF Postdoctoral Research Fellowship in Biology for FY 2020, Integrative Research Investigating the Rules of Life Governing Interactions Between Genomes, Environment and Phenotypes. The fellowship supports research and training of the fellow that will contribute to the area of Rules of Life in innovative ways. This research will use modern technologies to predict how bacteria will evolve in response to environmental pressures, an ability necessary to understand the evolution of dangerous traits such as antibiotic resistance. While an organism’s genetic sequence records its life history, it is difficult to predict how the organism will evolve based on sequence alone. This is due to the inability to predict how mutations can affect the organism’s ability to reproduce, termed its fitness. The project will use recently discovered biological laws to explore how mutations alter the cell’s energy budget to make new proteins. This information will be used to produce a mathematical model to understand how mutations are tied to fitness. The work will develop an experimental system where bacterial evolution can be controlled and predicted, which may have real world applications in medicine and agriculture. This project will augment the fellow’s diverse background through the application of new technology. To broaden the impact of the work, the fellow will train undergraduate and graduate students in the sponsoring scientists’ labs.Recently developed bacterial “growth laws” predict a strong correlation between the cellular growth rate and the proteomic fraction dedicated to ribosomes. This implies that adaptive evolution should skew the allocation of cellular resources towards maintaining ribosomes rather than unnecessary proteins. The objective of this work is to dissect how adaptive mutations modulate the allocation of resources to maximize growth rate, culminating in a mathematical model to predict evolution. The fellow will rely on novel sequencing-based experiments that permit time-resolved measurement of the emergence of novel beneficial mutations in large microbial populations. The effect of these beneficial mutations on the proteomic composition will be monitored via mass spectrometry and RNA-sequencing, illuminating how beneficial mutations alter the allocation of resources. Given a pairwise mapping of the identity of a beneficial mutation and the gene expression profile, the fellow aims to quantitatively map the fitness landscape. It then becomes possible to predict how changes in gene expression lead to changes in fitness. The fellow’s background in modeling and experimentation will be strengthened by the development and application of sequencing-based experiments. The broader impacts will include the training of undergraduate and graduate students in the design, execution, and interpretation of project experiments.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.